Towards High-Fidelity ECG Generation: Evaluation via Quality Metrics and Human Feedback

Maria Russo, Joana Rebelo, Nuno Bento, Hugo Gamboa, Hugo Gamboa

2025

Abstract

Access to medical data, such as electrocardiograms (ECGs), is often restricted due to privacy concerns and data scarcity, posing challenges for research and development. Synthetic data offers a promising solution to these limitations. However, ensuring that synthetic medical data is both realistic and clinically relevant requires evaluation methods that go beyond general quality metrics. This study aims to overcome such challenges by advancing high-fidelity ECG data generation and evaluation, presenting an approach for generating realistic ECG signals using a diffusion model and introducing a novel evaluation metric based on a deep learning evaluator model. The state-of-the-art Structured State Space Diffusion (SSSD-ECG) model was refined through hyperparameter optimization, and the fidelity of the generated signals was assessed using quantitative metrics and expert feedback. Complementary evaluations of diversity and utility ensured a comprehensive assessment. The evaluator model was developed to classify individual synthetic ECG signals into four quality classes and was trained on a custom-developed quality dataset designed for the generation of 12-lead ECG signals. Results demonstrated the success in generating high-fidelity ECG data, validated by evaluation metrics and expert feedback. Correlation studies confirmed an alignment between the evaluator model and fidelity metrics, highlighting its potential as a valid tool for quality assessment.

Paper Citation

in Harvard Style

Russo M., Rebelo J., Bento N. and Gamboa H. (2025). Towards High-Fidelity ECG Generation: Evaluation via Quality Metrics and Human Feedback. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: SyntBioGen; ISBN 978-989-758-731-3, SciTePress, pages 1154-1165. DOI: 10.5220/0013400500003911

in Bibtex Style

@conference{syntbiogen25,
author={Maria Russo and Joana Rebelo and Nuno Bento and Hugo Gamboa},
title={Towards High-Fidelity ECG Generation: Evaluation via Quality Metrics and Human Feedback},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: SyntBioGen},
year={2025},
pages={1154-1165},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013400500003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: SyntBioGen
TI - Towards High-Fidelity ECG Generation: Evaluation via Quality Metrics and Human Feedback
SN - 978-989-758-731-3
AU - Russo M.
AU - Rebelo J.
AU - Bento N.
AU - Gamboa H.
PY - 2025
SP - 1154
EP - 1165
DO - 10.5220/0013400500003911
PB - SciTePress